Assessing Human Pose Estimation Models for Clinically Relevant Body Segment Measurements

Anthropometric measurements are used for numerous biomedical applications, such as the management of dietary, medication, and orthopedic treatments. However, for some patients, these measures can be impaired due to the high complexity of their clinical condition with limitations and difficulties related to immobilization. Human Pose Estimation (HPE) models can be useful as an alternative anthropometric method that allows measuring individual body segments. This study aimed to compare the performance of HPE models for assessing limb length. A physiotherapist evaluated arms, forearm, trunk, and legs lengths in fifty-nine participants using both tape and five HPE models: PoseNet and four variations of MoveNet. Participants were positioned standing in front of a backdrop while photos were captured using the NLMeasurer mobile application. NLMeasurer is a smartphone application able to record postural, goniometric, and limb length assessments based on the identification of Key Joint Points (KJPs) performed by HPE models. The bias and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the performance of the models was similar, with an average error of 3.5 cm across most segments. However, the models' performance declined for hip-knee distance measurements, with errors ranging from 7.34 to 13.57 cm. HPEs showed promise for facilitating limb length assessments in an automated way, but they have not proven to be accurate enough to be incorporated into clinical practice and used without any human intervention.